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Tim Alberdingk Thijm will present his FPO "Modular Control Plane Verification" on Friday, February 9, 2024 in CS 105 (and Zoom).
by Gradinfo 12 Jan '24

12 Jan '24

Tim Alberdingk Thijm will present his FPO "Modular Control Plane Verification" on Friday, February 9, 2024 in CS 105 (and Zoom). Zoom link: https://princeton.zoom.us/j/95302742818 The members of his committee are as follows: - Advisor: Aarti Gupta - Reader: David Walker - Reader: Ryan Beckett (Microsoft Research) - Examiner: Maria Apostolaki (ECE) - Examiner: Zak Kincaid Please see talk abstract below. All are welcome to attend. Abstract: Networks continue to experience costly outages due to misconfigured control planes. Avoiding misconfigurations is challenging: control planes often use notoriously inscrutable distributed routing protocols. One remedy is control plane verification, which analyzes control planes to uncover violations of desired properties. Unfortunately, most verification tools analyze the entire network at once. Hence, these monolithic tools often do not scale well as networks grow in size and properties increase in complexity, or compromise on the networks and properties they support. To address these limitations, we explore modular control plane verification, where the user divides their network into fragments to verify independently, and annotates each fragment with an interface. These interfaces summarize each fragment’s routing announcements (a.k.a. routes). We prove that if each fragment guarantees its interface, assuming the interfaces of its neighbors, then properties of the fragments’ routes hold of the monolithic network’s. We present Kirigami, a formal model of network fragments where users specify interfaces as cuts dividing the network into fragments, annotating fragment boundaries with nodes’ stable routes. We define a Satisfiability Modulo Theories (SMT)-based procedure for checking interfaces, implemented as an extension to the NV (monolithic) verification tool. Kirigami’s SMT checks are up to five orders of magnitude faster than NV’s for a range of industrial topologies with synthesized network policies. Unfortunately, Kirigami’s interfaces are not change-resilient if network updates change fragments’ stable routes. However, if we naïvely extend Kirigami’s interfaces to use over-approximate sets of routes instead of exact routes, the resulting verification procedure allows unsound circular reasoning. To prevent circularity, we introduce a new model, Timepiece, where interfaces are defined using temporal invariants inspired by temporal logic. We implement a new SMT-based checking procedure, which scales to verify networks with thousands of nodes in minutes (compared to hours for a monolithic baseline) and allows users to write change-resilient interfaces.

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Naorin Hossain will present her FPO "Navigating Emerging Complexities of Modern Systems: Advancements in Automated Verification and Security Techniques" in CS 302 on January 11, 2024 at 9am.
by Gradinfo 11 Dec '23

11 Dec '23

Naorin Hossain will present her FPO "Navigating Emerging Complexities of Modern Systems: Advancements in Automated Verification and Security Techniques" in CS 302 on January 11, 2024 at 9am. The members of her committee are as follows: Margaret Martonosi (advisor, examiner) Aarti Gupta (reader) Pradip Bose (reader, IBM) Amit Levy (examiner) David August (examiner) Everyone is invited to attend the talk. Please see abstract below. Abstract: The increasing design complexity at the end of Moore’s Law and Dennard Scaling presents a new challenge for implementing modern systems correctly and securely. This dissertation presents focused efforts for advancing automated verification and security techniques to mitigate incorrect implementations and attackers, respectively. It explores two avenues: 1) correctness verification in virtual memory systems, where shared memory interactions occur across hardware and system-level events, and 2) enhancing security of heterogeneous systems-on-a-chip (SoCs) with anomalous activity detection and localization. In the first thrust, this dissertation develops tools for a systematic, end-to-end formalized approach for verifying and validating virtual memory system implementations using memory transistency model (MTM) specifications. First, I propose a novel formal language for reasoning about MTMs, enabling specification of the software-visible impacts of complex hardware- and system-level interactions that occur in parallel systems with virtual memory. Next, I present empirical MTM testing techniques for verification and validation on real system implementations using specialized stress measures to coax out MTM-specific bugs. The proposed techniques can alleviate complex system verification efforts by automating detection of bugs and vulnerabilities at the hardware-system interface of virtual memory systems. The second thrust of this dissertation develops processes to enhance security of heterogeneous SoC designs. Sophisticated attacks can take down parts of the SoC, resulting in diminished performance gains and failed processes. To design built-in defenses against such attacks, this dissertation proposes the use of network-on-chip (NoC) based hardware counters to monitor ongoing SoC activity in a holistic fashion. With these counters, I develop and demonstrate an anomalous activity detection and localization system to detect and pinpoint availability attacks on SoC components, leveraging machine learning techniques and the inherent interpretability offered by the NoC counters. These techniques enable automated attack and failure detection to be built into the SoC, a particularly valuable feature in the fast-changing heterogeneous SoC design space. Overall, this dissertation advocates for the use of formal and empirical modeling tools to effectively capture complex system behaviors such that vulnerabilities, bugs, and threats can be detected at design time and runtime with automated methods that provide elevated coverage and accuracy.

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TODAY @ 5:30: Professor Cynthia Dwork, Harvard
by Emily C. Lawrence 07 Dec '23

07 Dec '23

REMINDER: Thursday, December 7 at 5:30pm: Princeton Public Lectures presents, Cynthia Dwork “It’s in Your Phone. It’s in Your Browser. It’s in Your Redistricting Data! … It’s Differential Privacy.” In-person event in McCosh Hall, Room 50 To participate virtually, you may either register to watch via [ https://princeton.zoom.us/webinar/register/WN_UDMs-l99RX-Czc6K3EbtdQ | Zoom Webinar ] , or Watch via Princeton’s Media Central (Live-Stream feed) by [ https://mediacentrallive.princeton.edu/ | clicking here ] . For more information about this event, [ https://lectures.princeton.edu/lectures/2023/cynthia-dwork | click here ] .

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Thursday, Dec 7 @ 5:30: Professor Cynthia Dwork, Harvard
by Emily C. Lawrence 01 Dec '23

01 Dec '23

Thursday, December 7 at 5:30pm: Princeton Public Lectures presents, Cynthia Dwork “It’s in Your Phone. It’s in Your Browser. It’s in Your Redistricting Data! … It’s Differential Privacy.” In-person event in McCosh Hall, Room 50 To participate virtually, you may either register to watch via [ https://princeton.zoom.us/webinar/register/WN_UDMs-l99RX-Czc6K3EbtdQ | Zoom Webinar ] , or Watch via Princeton’s Media Central (Live-Stream feed) by [ https://mediacentrallive.princeton.edu/ | clicking here ] . For more information about this event, [ https://lectures.princeton.edu/lectures/2023/cynthia-dwork | click here ] .

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DATE CORRECTION: ML Theory Seminar on neural network approximation by Prof. Song Mei from UC Berkeley
by Emily C. Lawrence 30 Nov '23

30 Nov '23

ML Theory Seminar Prof. Song Mei from UC Berkeley Statistics is giving a seminar talk on neural network approximation theory in the modern age. The talk will be on Friday (Dec 8th) 12:30 pm -1:30 pm at Equad B205. =============================================================== Title: Revisiting neural network approximation theory in the age of generative AI Abstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it inadequately explains the impressive capabilities of modern generative AI models such as language models and diffusion models. This talk puts forth a refined perspective: neural networks often serve as algorithm approximators, going beyond mere function approximation. I will explain how this refined perspective offers a deeper insight into the success of modern generative AI models. Bio: Song Mei is an assistant professor of statistics and EECS at UC Berkeley. He received his Ph.D. from Stanford in June 2020. Song’s research lies at the intersection of statistics and machine learning. His recent research focuses on mathematics of deep learning and foundation models, theory of reinforcement learning, and high dimensional statistics.

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ML Theory Seminar on neural network approximation by Prof. Song Mei from UC Berkeley
by Emily C. Lawrence 29 Nov '23

29 Nov '23

ML Theory Seminar Prof. Song Mei from UC Berkeley Statistics is giving a seminar talk on neural network approximation theory in the modern age. The talk will be on Friday (Dec 1st) 12:30 pm -1:30 pm at Equad B205. =============================================================== Title: Revisiting neural network approximation theory in the age of generative AI Abstract: Textbooks on deep learning theory primarily perceive neural networks as universal function approximators. While this classical viewpoint is fundamental, it inadequately explains the impressive capabilities of modern generative AI models such as language models and diffusion models. This talk puts forth a refined perspective: neural networks often serve as algorithm approximators, going beyond mere function approximation. I will explain how this refined perspective offers a deeper insight into the success of modern generative AI models. Bio: Song Mei is an assistant professor of statistics and EECS at UC Berkeley. He received his Ph.D. from Stanford in June 2020. Song’s research lies at the intersection of statistics and machine learning. His recent research focuses on mathematics of deep learning and foundation models, theory of reinforcement learning, and high dimensional statistics.

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TODAY: CSML SEMINAR and ECE KORHAMMER SEMINAR SERIES: Haifeng Xu, University of Chicago
by Emily C. Lawrence 29 Nov '23

29 Nov '23

CSML SEMINAR ECE KORHAMMER SEMINAR SERIES Speaker: Haifeng Xu, University of Chicago Title: Algorithms and Incentives in Machine Learning Day: Wednesday, November 29, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Hosts: Chi Jin and Mengdi Wang Abstract: Machine learning designs approaches that transform data to predictions or estimations. The standard paradigm often assumes these data are objectively generated from distributions, without being affected by any human factors. However, this paradigm ceases to be true when our predictions or estimated parameters will in turn affect the data providers' welfare. In such situations, data providers have incentives to alter the data for their own benefits. Thus the design of any learning methods must account for potential data manipulations due to data providers' incentives. This talk will introduce a general ""incentive-aware"" framework for designing prediction methods. I will illustrate this design paradigm with two examples: (1) a very recent and timely application of eliciting authors' truthful private information for improving the peer review systems for today's massive scale machine learning conferences; (2) a very classic problem of PAC-learning classifiers but with strategic providers of data features. In both problems, I will illustrate how the presence of incentives can fundamentally change the problem's learning efficiency and how algorithms can help to overcome some statistic barriers. Bio: Haifeng Xu is an assistant professor in the Department of Computer Science and Data Science Institute at UChicago. He directs the Sigma Lab (Strategic IntelliGence for Machine Agents) which focuses on designing intelligent AI systems that can effectively learn and act in informationally complex multi-agent setups. His research has been recognized by a few awards including IJCAI early career spotlight, Google Faculty research award, the ACM SIGecom Dissertation Award and IFAAMAS Victor Lesser Distinguished Dissertation Award. This seminar is supported with funds from the Korhammer Lecture Series.

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Dr. Fei-Fei Li in Conversation with Provost Jennifer Rexford Wednesday, Nov 29 at 6pm
by Emily C. Lawrence 29 Nov '23

29 Nov '23

Wednesday, November 29 at 6pm Princeton Public Lectures presents, Dr. Fei-Fei Li in Conversation with Jennifer Rexford in-person event in McCosh 50 Free copies of Dr. Li's book, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI, will be handed out to the first 300 in-person attendees. To participate virtually, you may either register to watch via [ https://princeton.zoom.us/webinar/register/WN_L58PDluYR_K6huylyTPiPg | Zoom Webinar ] , or Watch via Princeton’s Media Central (Live-Stream feed) by [ https://mediacentrallive.princeton.edu/ | clicking here ] . For more information about this event, [ https://lectures.princeton.edu/lectures/2023/dr-fei-fei-li | click here ] .

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Princeton Robotics Seminar: Sonia Chernova, Friday, Dec 1 at 11am
by Emily C. Lawrence 28 Nov '23

28 Nov '23

Speaker: Sonia Chernova, Georgia Tech Date: Friday, December 1, 2023 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Autonomy in the Human World: Developing Robots that Handle the Diversity of Human Lives Abstract: Reliable operation in everyday human environments – homes, offices, and businesses – remains elusive for today’s robotic systems. A key challenge is diversity, as no two homes or businesses are exactly alike. However, despite the innumerable unique aspects of any home, there are many commonalities as well, particularly about how objects are placed and used. These commonalities can be captured in semantic representations, and then used to improve the autonomy of robotic systems by, for example, enabling robots to infer missing information in human instructions, efficiently search for objects, or manipulate objects more effectively. In this talk, I will discuss recent advances in semantic reasoning, particularly focusing on semantics of everyday objects, household environments, and the development of robotic systems that intelligently interact with their world. Bio: Sonia Chernova is an Associate Professor in the College of Computing at Georgia Tech. She directs the Robot Autonomy and Interactive Learning lab, where her research focuses on the development of intelligent and interactive autonomous systems. Chernova’s contributions span robotics and artificial intelligence, including semantic reasoning, adaptive autonomy, human-robot interaction, and explainable AI. She also leads the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), whose mission is to develop collaborative AI partners-in-care that help support a growing population of older adults, helping them sustain independence, improve quality of life, and increase effectiveness of care coordination across their care network.

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Udaya Ghai will present his FPO "A Game-theoretic Lens for Robustness in Control" on Monday, December 18, 2023 at 4:00 PM in CS 402.
by CS Grad Department 21 Nov '23

21 Nov '23

Udaya Ghai will present his FPO " A Game-theoretic Lens for Robustness in Control " on Monday, December 18, 2023 at 4:00 PM in CS 402 . Location: CS 402 The members of Udaya ’s committee are as follows: Examiners: Elad Hazan (Adviser), Ryan Adams , Naomi Leonard Readers: Sanjeev Arora , Anirudha Majumdar A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: The control of dynamical systems is a fundamental problem with a vast array of applications, from robotics to biological engineering. Recently, the game-theoretic primitive of regret minimization has been applied to control, yielding novel instance-optimal performance guarantees in more challenging non-stochastic control settings. This thesis further explores the benefits of a multi-agent perspective of control. Concretely, we begin with a new algorithm for generating disturbances for controller verification, which relies on recasting the players in the nonstochastic control game. Next, we provide a cooperative multi-agent extension of the nonstochastic control setting, involving a reduction from our multi-agent game to single agent regret minimization. Furthermore, we show new notions of robustness to failure can be attained through this perspective, even in a single-agent setting. While control is a powerful tool, it relies heavily on knowledge of the dynamics. The final chapters provide two very different approaches to robustness without such a model. The first approach extends the nonstochastic control methodology to model-free reinforcement learning. In an alternative approach, we consider unknown systems with dynamics that are \emph{approximately} linear using tools from classical control theory.

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